The Evolved
Marketer
Skills, Not New Job Titles: What AI-Ready B2B Marketing Teams Actually Look Like
Is marketing actually changing — or does it just look like it is?
The job titles landing in my feed right now — Marketing Engineer, GTM Engineer, AI-Native Marketer — suggest we need an entirely new kind of person. New role, new title, new hiring budget.
I'm not convinced.
I've been sitting with a question that matters to me both professionally and practically: if you were asked to build a B2B marketing team today — 20 to 40 people, simple product stack, not matrixed — what would actually change? What stays? And what does "AI-native" actually mean when you're hiring a real person to do a real job?
What I keep coming back to: the fundamentals of great marketing haven't changed. What's changed is the execution layer on top of them. Conflating those two things — treating a skills evolution like a role revolution — is how you end up with a new silo where the old one used to be.
This resource is my working answer to that question. It includes a real job description I hired for, annotated to show where it should evolve with AI. I'll keep updating it as my thinking evolves. Reactions welcome.
— AMG
The best marketers have always done one thing: take timely insight and turn it into a decision for a buyer.
The marketing we put into the world needs to be helpful. Not clever for its own sake. Not volume for the sake of the algorithm. Helpful — meaning it gives a buyer more insight into their own business, their customer, or the industry in which they operate.
That job hasn't changed. What makes a good insight? What makes that insight worth turning into content? What format does a specific buyer actually need to make a decision? These are skills that take time and repetition to hone. No model has them. The marketer does.
When I think about what makes someone worth hiring into any core marketing role — campaigns, content, ops — the list is consistent regardless of what tools exist:
The marketer who has these qualities and learns AI will dramatically outperform the "Marketing Engineer" who can build workflows but can't write an insight.
Here's a job description I hired for. Here's how it should change — and how much it doesn't.
This is a Content Marketing Director role from a previous team. The original language is intact in the left column. The right column shows where the role evolves with AI — new or changed requirements are marked in red.
The takeaway I'd ask you to notice: the changes are additive. The core job doesn't transform into something unrecognisable. It asks more of the same person.
A seasoned Sr. Director of Content Marketing with a strong background in technology and B2B storytelling to create high-impact, data-driven content that supports growth, education, and retention goals. Leads a team of content and creative writers to establish what excellent content marketing looks like.
The leader we seek might have a journalistic background or be relentless in their pursuit of no-jargon, no-B.S. content. You have an eye for compelling narratives and the ability to translate complex insights into engaging and informative content.
A seasoned Sr. Director of Content Marketing with a strong background in technology and B2B storytelling to create high-impact, data-driven content that supports growth, education, and retention goals. Leads a team of content and creative writers to establish what excellent content marketing looks like.
The leader we seek might have a journalistic background or be relentless in their pursuit of no-jargon, no-B.S. content. You have an eye for compelling narratives and the ability to translate complex insights into engaging and informative content.
Sets the standard for AI-assisted content production across the team — including what "good" looks like when AI is in the workflow, and how to maintain brand voice and editorial judgment at scale.
- Craft high-quality, compelling B2B content — customer stories, articles, research reports, and case studies that educate and engage a tech-savvy audience.
- Develop clear, persuasive, and data-informed narratives that support growth initiatives, customer education, and retention.
- Collaborate with product, marketing, and sales teams to align content with business priorities and communicate complex information effectively.
- Craft high-quality, compelling B2B content — customer stories, articles, research reports, and case studies that educate and engage a tech-savvy audience.
- Develop clear, persuasive, and data-informed narratives that support growth initiatives, customer education, and retention.
- Collaborate with product, marketing, and sales teams to align content with business priorities and communicate complex information effectively.
- Uses AI as a co-pilot for ideation, research synthesis, and first-draft generation — exercises editorial judgment to evaluate, elevate, and course-correct AI outputs before they reach an audience.
- Defines and maintains brand voice standards that can be used to brief AI tools and evaluate their outputs; can articulate precisely when an AI output violates those standards and why.
- Leverage data from multiple sources to uncover insights and inform content topics that resonate with target audiences.
- Comfortable working directly in analytics to assess content performance and audience engagement, adjusting strategy based on insights and trends.
- Interpret industry research, survey findings, and insights to create content that speaks to key challenges and opportunities.
- Leverage data from multiple sources to uncover insights and inform content topics that resonate with target audiences.
- Comfortable working directly in analytics to assess content performance and audience engagement, adjusting strategy based on insights and trends.
- Interpret industry research, survey findings, and insights to create content that speaks to key challenges and opportunities.
- Fluent in AI-assisted research and competitive intelligence workflows — can build and run repeatable processes for market monitoring without depending on a dedicated analyst or agency.
- Understands how AI search (GEO/AEO) affects content visibility — tracks how their category and company are being represented in AI-generated answers and takes an active role in shaping that narrative.
- Develop editorial calendars and content roadmaps that support key marketing goals and customer journeys.
- Identify content gaps and opportunities for SEO-optimised content that addresses audience needs and drives organic traffic.
- Experiment with content formats, headlines, and angles to optimise engagement, conversion, and retention.
- Develop editorial calendars and content roadmaps that support key marketing goals and customer journeys.
- Identify content gaps and opportunities for SEO-optimised content that addresses audience needs and drives organic traffic.
- Experiment with content formats, headlines, and angles to optimise engagement, conversion, and retention.
- Incorporates GEO (Generative Engine Optimisation) alongside traditional SEO — content is built to be cited and accurately surfaced by AI search, not just indexed by traditional engines.
- Can build and manage lightweight AI-assisted content workflows that scale production without sacrificing quality or brand consistency.
- Degree in Journalism, Communications, Marketing, or a related field.
- Several years of experience in content writing with a focus on technology, B2B, or similarly complex audiences.
- Exceptional writing, editing, and storytelling skills with a high degree of creativity and attention to detail.
- Strong analytical skills; proficiency with analytics tools (e.g. Google Analytics).
- Demonstrated experience in content strategy, SEO, and digital marketing metrics.
- Familiarity with the technology industry, SaaS, or enterprise software.
- Ability to manage multiple projects with competing deadlines.
- Degree in Journalism, Communications, Marketing, or a related field.
- Several years of experience in content writing with a focus on technology, B2B, or similarly complex audiences.
- Exceptional writing, editing, and storytelling skills with a high degree of creativity and attention to detail.
- Strong analytical skills; proficiency with analytics tools (e.g. Google Analytics).
- Demonstrated experience in content strategy, SEO, and digital marketing metrics.
- Familiarity with the technology industry, SaaS, or enterprise software.
- Ability to manage multiple projects with competing deadlines.
- Demonstrated ability to work with AI writing tools: prompt craft, brand-voice evaluation, output quality assessment. Not looking for a coder — looking for someone with informed, opinionated judgment about what AI can and can't do in a content workflow.
- Familiarity with GEO/AEO principles and how generative search affects content distribution and discoverability.
- Experience building or managing AI-assisted content workflows — at the process level, not the code level.
- Can coach and model new AI skills for their team without creating dependency on a single "AI person."
The new requirements are additive. The same candidate who was excellent before is still the one you want — they've just been asked to grow in a specific direction.
What changes role by role — the skills that stay, and the new hard skills each marketer needs to acquire.
This isn't exhaustive — these are three core roles in a mid-size B2B marketing team, and my read on how the capability profile changes. I share this to provoke reactions, not to suggest I have all the answers. What are you seeing in your team?
- Narrative instinct — knowing which story is worth telling
- Insight translation — turning data and research into something a buyer actually cares about
- Audience empathy — understanding the reader's context and decision process
- Editorial judgment — knowing when something is good, and what specifically is wrong when it isn't
- Voice consistency — the ability to write and maintain a brand's voice across formats
- Prompt craft — writing effective, specific AI briefs for content tasks
- AI output evaluation — identifying where AI goes wrong and articulating why, at the specificity needed to course-correct
- GEO/AEO literacy — understanding how generative search surfaces content and building for it
- Content workflow automation — building repeatable production workflows with AI in the loop
- Knowledge management — building and maintaining the Brand OS context that makes AI outputs better over time
- Systems thinking — seeing how workflows connect and where they break
- Data hygiene and governance — the unglamorous work that makes everything downstream trustworthy
- Attribution and measurement — building models that actually explain what's driving pipeline
- Process design — creating workflows that scale without depending on any one person
- Cross-functional connective tissue — making sales, marketing, and finance speak the same language
- AI workflow building — multi-step automated workflows that use AI at decision points, not just task execution
- Prompt-based data analysis — querying data and generating insights using AI tools, reducing dependency on analysts for standard questions
- AI model integration — connecting AI tools to existing martech and understanding what data each model needs to produce reliable outputs
- Marketing intelligence systems — lightweight competitive and market monitoring pipelines that surface insights automatically
- Hypothesis-driven testing — knowing what to test, why, and how to read results
- Attribution thinking — understanding how to measure what actually drove a conversion
- Channel strategy — knowing where buyers are and how each channel behaves differently
- Budget management — the ability to allocate, reallocate, and defend spend with clear logic
- Copy and creative instinct — judgment about which message will land, even in a paid context
- AI creative testing — generating and testing copy and creative variations at scale, interpreting results with human context
- Predictive modeling basics — using AI-assisted tools to model audience behaviour, LTV, and channel performance
- AI-assisted audience segmentation — going deeper on ICP segments using AI analysis of behavioural, firmographic, and intent data
- Understanding AI in the buyer's journey — how buyers now use AI to research, compare, and evaluate options, and how that changes where and how you reach them
"Marketing Engineer" isn't a new role. It's a description of skills that should be distributed across the whole team.
The industry has started treating the AI capability gap in marketing as a recruitment problem. Post a new title, hire a specialist, solved. Firms tracking this space are counting thousands of "Marketing Engineer" and "GTM Engineer" openings and treating that volume as validation that the role makes sense.
I'd argue the volume reflects confusion, not clarity. Ask ten of those employers what the role actually does and you'll get ten different answers. Some want a developer who understands GTM. Some want a marketer who can write code. Some want a marketing ops person with a more impressive title. The definition is still fluid because the need is still being figured out.
Here's my concern with where this goes: we've built this silo before.
The alternative isn't refusing to acknowledge that AI requires new skills. It clearly does. The alternative is treating those skills as capabilities that distribute across the team — developed at different depths depending on the role.
A content marketer who deeply understands prompt craft and GEO is more valuable than a Marketing Engineer who builds pipelines but can't write an insight. A marketing ops person who can build AI workflows on top of clean data is more valuable than a specialist building agents on top of messy infrastructure.
The question for leaders isn't "who do I hire to be the AI person?" It's: "how do I build AI fluency into every person already on my team?"
That's harder than posting a job. It's also the only version that compounds.
The teams that win won't have the best Marketing Engineer. They'll have the most AI-fluent content team, ops team, and performance team — working from the same foundation.
Thinking through what this looks like for your team?
If this raised questions worth talking through — about your org structure, your team's skills gaps, or what an AI-ready marketing team actually needs to look like for your stage — I'm taking on a small number of engagements this quarter.